SUMMARY Individuals can respond to diverse nutrients and dietary restrictions in markedly different ways. Some people easily gain weight, but others remain thin no matter what they eat. Additionally, metabolic diseases can differ dramatically among individuals in a population, for both rare single-gene Mendelian diseases and common multifactorial metabolic diseases such as obesity and type 2 diabetes. In large part, this variability suggests that individual genetic differences greatly affect the likelihood to get sick as well as the severity of the illness for both rare and common metabolic diseases across a population. It would be extremely valuable if one could identify both rare and common variants that contribute to individual responses to diet and to the acquisition of different types of metabolic diseases. Rare variants are usually identified by linkage mapping and whole- genome sequencing using families with affected individuals. By contrast, common variants are usually identified by genome-wide association studies using large populations of people with and without a disease. We will develop personalized metabolic network models for a large set of genetic individuals of the nematode C. elegans, both representing healthy metabolic state and mimicking an inborn error of human metabolism. With our experimental system and approach we will be able to derive predictions of both rare and common variation in a variety of metabolic traits influenced by nutrition. We will extensively validate such predictions using CRISPR/Cas9-mediated genome editing.